7 citations · 14 across the 13 of their papers we have counts for
5 papers · 2 filters
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection
Yi Dai, Hao Lang, Kaisheng Zeng +2
Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning. Recent multi-modal OOD detection leverages textual information from in-distribution…
Long-Tailed Question Answering in an Open World
Yi Dai, Hao Lang, Yinhe Zheng +2
Real-world data often have an open long-tailed distribution, and building a unified QA model supporting various tasks is vital for practical QA applications. However, it is non-tri…
Domain Incremental Lifelong Learning in an Open World
Yi Dai, Hao Lang, Yinhe Zheng +3
Lifelong learning (LL) is an important ability for NLP models to learn new tasks continuously. Architecture-based approaches are reported to be effective implementations for LL mod…
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts
Hao Lang, Yinhe Zheng, Binyuan Hui +2
Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts. However, most previous OOD intent de…
A Survey on Out-of-Distribution Detection in NLP
Hao Lang, Yinhe Zheng, Yixuan Li +3
Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. Great progress has been made over the past year…